CAT Vehicle Program/ECE REUPI: K. Larry Head, PhD and Tamal Bose, PhDCoordinator: Nancy EmptageSponsor: The University of Arizona, National Science Foundation REU

Ronald E. McNair Achievement ProgramWILLIAM ANDERSONCognitive Engine Testbed for Vehicular Communications(Partners: Ashley Aponik and Youssef Daoud)Wofford College, Computer ScienceMentors: Dr. Tamal Bose and Noel Teku, Electrical Engineering

AbstractAd-hoc networks have the potential to increase the safety and reliability of autonomous vehicles. The amount of radio spectrum available for such networks is limited, however. The use of cognitive radio, especially when integrated with reinforcement learning algorithms, may help to ease the issue of limited spectrum by finding optimal transmission policies and detecting the presence of other users, especially in a scenario where a primary user and secondary user are contesting for spectrum. This paper presents a testbed for simulating cognitive engines in these networks using a variety of reinforcement learning algorithms, including ε-greedy, Softmax Strategy, and Q-Learning. The goal of these cognitive engines is to learn to choose the best modulation and coding rates given various channel models. The user can choose the desired channel model and optimization goal (i.e. maximize throughput, minimize bit error rate). The cognitive engine then learns the optimal coding rates and modulation schemes for the given environment, and the testbed displays the performance of each learning algorithm.

ASHLEY APONIKCognitive Engine Testbed for Vehicular Communications(Partners: Ashley Aponik and Youssef Daoud)Yale University, Electrical EngineeringMentors: Dr. Tamal Bose and Noel Teku, Electrical Engineering

AbstractAd-hoc networks have the potential to increase the safety and reliability of autonomous vehicles. The amount of radio spectrum available for such networks is limited, however. The use of cognitive radio, especially when integrated with reinforcement learning algorithms, may help to ease the issue of limited spectrum by finding optimal transmission policies and detecting the presence of other users, especially in a scenario where a primary user and secondary user are contesting for spectrum. This paper presents a testbed for simulating cognitive engines in these networks using a variety of reinforcement learning algorithms, including ε-greedy, Softmax Strategy, and Q-Learning. The goal of these cognitive engines is to learn to choose the best modulation and coding rates given various channel models. The user can choose the desired channel model and optimization goal (i.e. maximize throughput, minimize bit error rate).

The cognitive engine then learns the optimal coding rates and modulation schemes for the given environment, and the testbed displays the performance of each learning algorithm.

LANDON BENTLEYImplementation of Lane Detection and Lane Following in Autonomous Vehicles (Partners: Joe MacInnes and Hannah Mason)The University of Alabama, Comuter Science and MathematicsMentors: Dr. Tamal Bose and Rahul Bhadani, Electrical Engineering

AbstractLane detection and following are important aspects for the future of autonomous vehicles. This paper explores a comprehensive implementation for lane detection and following using hardware-in-the-loop testing on a hybrid autonomous vehicle. Hue, lightness, and saturation are used with a perspective transform to detect lanes from an image and produce a bird's eye view. A modification to existing path extraction algorithms is proposed that improves upon the shortcomings of current algorithms. The resulting algorithm generates waypoints from extracted lane markings with higher accuracy in curvy lanes with discontiguous markings. The resulting waypoints are sent to a Hoffmann steering controller which performs real-time trajectory correction. This paper details the implementation of these subcomponents and a comparison to the results of competing approaches.SAMANTHA HARRISThe Use of Verification Methods to Produce Safe Code for the CAT Vehicle(Partner: Levi Welch)Stetson UnversityMentors: Dr. Tamal Bose and Matt Bunting, Electrical Engineering

AbstractWhen developing a cyber-physical system (CPS), designers mainly concentrate on functionality and dynamics. As algorithms become more complex, multiple computers are needed to run the full system safely. This may result in undesirable outcomes such as an increase in cost or processing power. The objective of this research is to use verification methods to produce safe code for the CAT Vehicle, the autonomous vehicle that is being developed at the University of Arizona. The verification methods ensure that the network of the autonomous vehicle runs within four constraints. The four constraints are cost, processing power, bandwidth, and latency. Operating within these constraints allows the car to maximize its data processing potential. A Domain Specific Modeling Language (DSML) was designed using WebGME to produce a model of the network. The DSML uses an interpreter to generate template code and to verify the dynamic constraints. When constraints are violated, technique selectors rewire connections or exchange a computer within the network. When all

constraints are met, the information can be sent quickly and accurately to allow the vehicle to operate in real time. The purpose of this paper is to present a DSML with verification methods that allow the production of safe and efficient code to run the network system of the CAT Vehicle.

JOSEPH MaCINNESImplementation of Lane Detection and Lane Following in Autonomous Vehicles(Partners: Landon Bently and Hannah Mason)The College of Wooster, Computer ScienceMentors: Dr. Tamal Bose and Rahul Bhadani, Electrical Engineering

AbstractLane detection and following are important aspects for the future of autonomous vehicles. This paper explores a comprehensive implementation for lane detection and following using hardware-in-the-loop testing on a hybrid autonomous vehicle. Hue, lightness, and saturation are used with a perspective transform to detect lanes from an image and produce a bird's eye view. A modification to existing path extraction algorithms is proposed that improves upon the shortcomings of current algorithms. The resulting algorithm generates waypoints from extracted lane markings with higher accuracy in curvy lanes with discontiguous markings. The resulting waypoints are sent to a Hoffmann steering controller which performs real-time trajectory correction. This paper details the implementation of these subcomponents and a comparison to the results of competing approaches.

HANNAH MASONImplementation of Lane Detection and Lane Following in Autonomous Vehicles(Partners: Landon Bently and Joe MaCinnes)Lipscomb University, Computer EngineeringMentors: Dr. Tamal Bose and Rahul Bhadani, Electrical Engineering

AbstractLane detection and following are important aspects for the future of autonomous vehicles. This paper explores a comprehensive implementation for lane detection and following using hardware-in-the-loop testing on a hybrid autonomous vehicle. Hue, lightness, and saturation are used with a perspective transform to detect lanes from an image and produce a bird's eye view. A modification to existing path extraction algorithms is proposed that improves upon the shortcomings of current algorithms. The resulting algorithm generates waypoints from extracted lane markings with higher accuracy in curvy lanes with discontiguous markings. The resulting waypoints are sent to a Hoffmann steering controller which performs real-time trajectory correction. This paper details the implementation of these subcomponents and a comparison to the results of competing

approaches.

LEVI WELCHThe Use of Verification Methods to Produce Safe Code for the CAT Vehicle(Partner: Samantha Harris)University of Michigan, Electrical EngineeringMentors: Dr. Tamal Bose and Matt Bunting, Electrical Engineering

AbstractWhen developing a cyber-physical system (CPS), designers mainly concentrate on functionality and dynamics. As algorithms become more complex, multiple computers are needed to run the full system safely. This may result in undesirable outcomes such as an increase in cost or processing power. The objective of this research is to use verification methods to produce safe code for the CAT Vehicle, the autonomous vehicle that is being developed at the University of Arizona. The verification methods ensure that the network of the autonomous vehicle runs within four constraints. The four constraints are cost, processing power, bandwidth, and latency. Operating within these constraints allows the car to maximize its data processing potential. A Domain Specific Modeling Language (DSML) was designed using WebGME to produce a model of the network. The DSML uses an interpreter to generate template code and to verify the dynamic constraints. When constraints are violated, technique selectors rewire connections or exchange a computer within the network. When all constraints are met, the information can be sent quickly and accurately to allow the vehicle to operate in real time. The purpose of this paper is to present a DSML with verification methods that allow the production of safe and efficient code to run the network system of the CAT Vehicle.

DAOUD YOUSSEFCognitive Engine Testbed for Vehicular Communications(Partners: William Anderson and Ashley Aponik)Tennessee Technological University, Electrical EngineeringMentors: Dr. Tamal Bose and Noel Teku, Electrical Engineering

AbstractAd-hoc networks have the potential to increase the safety and reliability of autonomous vehicles. The amount of radio spectrum available for such networks is limited, however. The use of cognitive radio, especially when integrated with reinforcement learning algorithms, may help to ease the issue of limited spectrum by finding optimal transmission policies and detecting the presence of other users, especially in a scenario where a primary user and secondary user are contesting for spectrum. This paper presents a testbed for simulating cognitive engines in these networks using a variety of reinforcement learning algorithms, including ε-greedy, Softmax

Strategy, and Q-Learning. The goal of these cognitive engines is to learn to choose the best modulation and coding rates given various channel models. The user can choose the desired channel model and optimization goal (i.e. maximize throughput, minimize bit error rate). The cognitive engine then learns the optimal coding rates and modulation schemes for the given environment, and the testbed displays the performance of each learning algorithm.